class_docstr: SGD - Stochastic Gradient Descent optimizer.
  
  Attributes
  ----------
  lr: float
      The learning rate that controls learning step size. Adjustable in progress, default: 0.01.
  batch: int
      The mini-batch size, number of examples used to compute single gradient step, default: 10.
  momentum: float
      The momentum factor that helps accelerate gradients vectors in the right direction, default 0.
  
  Methods
  -------
  set_lr(value, min, max)
      Set value for learning rate.
  set_batch(value, allow_set)
      Set value for batch size.
  set_momentum(value, min, max)
      Set value for momentum.
code: def lr(self):
      return self._lr
  
label_desc: Use image node_id 5 for calling the SgdParams obj's underlying member method code with example usage: obj.lr() and returns: self